The final performance of the RR+RA model with optimised hyperparameters was compared to that of the state-of-the-art BI-R "baseline" model, using the optimised hyperparameters as stated by van de Ven et al [1]}. Given that CF directly reduces the final classification precision of previously seen classes, this was main metric employed as an indicator of continual learning performance. All results stated relate to the average test precision across 5 runs of the experiment with optimal hyperparameters. Due to the nature of CF, it will tend to have a greater detrimental effect on the learned representations of earlier-seen classes, whose internal representations are more likely to be 'over-written'. Thus, the average precision on the first 50% of classes seen will tend to be lower than the average over the last 50% and any successful method will likely lead to an improvement, not necessarily in the overall average precision, but in the average precision of the first 50% and most likely the first 20% of classes.
